AI Made for Military May Aid Bipolar Treatment

New research discovers that a machine learning application designed for the military can also be used to predict treatment outcomes for bipolar disorder.

Researchers at the University of Cincinnati (UC) conducted the medical study using the application originally developed for air-to-air combat. The successful use of machine based fuzzy logic opens the possibility for using AI, or machine learning, to treat disease.

In the study, Dr. David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called “genetic fuzzy trees” to predict how bipolar patients would respond to lithium.

Bipolar disorder, depicted in the TV show “Homeland” and the Oscar-winning “Silver Linings Playbook,” affects as many as six million adults in the United States or four percent of the adult population in a given year.

“In psychiatry, treatment of bipolar disorder is as much an art as a science,” Fleck said.

“Patients are fluctuating between periods of mania and depression. Treatments will change during those periods. It’s really difficult to treat them appropriately during stages of the illness.”

In the study, researchers found the best of eight common models used to currently treat bipolar disorder, predicted who would respond to lithium treatment with 75 percent accuracy.

By comparison, the model UC researchers developed using AI predicted how patients would respond to lithium 100 percent of the time. Even more impressively, the UC model predicted the actual reduction in manic symptoms after lithium treatment with 92 percent accuracy.

It turns out that the same kind of artificial intelligence that outmaneuvered Air Force pilots last year in simulation after simulation at Wright-Patterson Air Force Base is equally adept at making beneficial decisions that can help doctors treat disease.

The findings appear in the journalÂ Bipolar Disorders.

“What this shows is that an effort funded for aerospace is a game-changer for the field of medicine. And that is awesome,” said Dr, Kelly Cohen, a professor in UC’s College of Engineering and Applied Science.

Cohen’s doctoral graduate Nicholas Ernest is founder of the company Psibernetix, Inc., an artificial intelligence development and consultation company.

Psibernetix is working on applications such as air-to-air combat, cybersecurity, and predictive analytics. Ernest’s fuzzy logic algorithm is able to sort vast possibilities to arrive at the best choices in literally the blink of an eye.

His team developed a genetic fuzzy logic called Alpha capable of shooting down human pilots in simulations, even when the computer’s aircraft intentionally was handicapped with a slower top speed and less nimble flight characteristics.

The system’s autonomous real-time decision-making shot down retired U.S. Air Force Col. Gene Lee in every engagement.

“It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment,” Lee said last year. “It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed.”

The American Institute of Aeronautics and Astronautics honored Cohen and Ernest this year for their “advancement and application of artificial intelligence to large scale, meaningful and challenging aerospace-related problems.”

Cohen spent much of his career working with fuzzy-logic based AI in drones. He used a sabbatical from the engineering college to approach the UC College of Medicine with an idea: What if they could apply the amazing predictive power of fuzzy logic to a particularly nettlesome medical problem?

Medicine and avionics have little in common. But each entails an ordered process — a vast decision tree — to arrive at the best choices.

Fuzzy logic is a system that relies not on specific definitions but generalizations to compensate for uncertainty or statistical noise. This artificial intelligence is called “genetic fuzzy” because it constantly refines its answer, tossing out the lesser choices in a way analogous to the genetic processes of Darwinian natural selection.

Cohen compares it to teaching a child how to recognize a chair. After seeing just a few examples, any child can identify the object people sit in as a chair, regardless of its shape, size or color.

“We do not require a large statistical database to learn. We figure things out. We do something similar to emulate that with fuzzy logic,” Cohen said.

Cohen found a receptive audience in Fleck, who was working with UC’s former Center for Imaging Research. After all, who better to tackle one of medical science’s hardest problems than a rocket scientist? Cohen, an aerospace engineer, felt up to the task.

Ernest said people should not conflate the technology with its applications. The algorithm he developed is not a sentient being like the villains in the “Terminator” movie franchise but merely a tool, he said, albeit a powerful one with seemingly endless applications.

Ernest’s company created EVE, a genetic fuzzy AI that specializes in the creation of other genetic fuzzy AIs. EVE came up with a predictive model for patient data called the LITHium Intelligent Agent or LITHIA for the bipolar study.

“This predictive model taps into the power of fuzzy logic to allow you to make a more informed decision,” Ernest said. And unlike other types of AI, fuzzy logic can describe in simple language why it made its choices, he said.

The researchers teamed up with Dr. Caleb Adler, the UC Department of Psychiatry and Behavioral Neuroscience vice chairman of clinical research, to examine bipolar disorder, a common, recurrent and often lifelong illness. Despite the prevalence of mood disorders, their causes are poorly understood, Adler said.

“Really, it’s a black box,” Adler said. “We diagnose someone with bipolar disorder. That’s a description of their symptoms. But that doesn’t mean everyone has the same underlying causes.”

Selecting the appropriate treatment can be equally tricky.

“Over the past 15 years there has been an explosion of treatments for mania. We have more options. But we don’t know who is going to respond to what,” Adler said. “If we could predict who would respond better to treatment, you would save time and consequences.”

With appropriate care, bipolar disorder is a manageable chronic illness for patients whose lives can return to normal, he said.

UC’s new study, funded in part by a grant from the National Institute of Mental Health, identified 20 patients who were prescribed lithium for eight weeks to treat a manic episode. Fifteen of the 20 patients responded well to the treatment.

The algorithm used an analysis of two types of patient brain scans, among other data, to predict with 100 percent accuracy which patients responded well and which didn’t. And the algorithm also predicted the reductions in symptoms at eight weeks, an achievement made even more impressive by the fact that only objective biological data were used for prediction rather than subjective opinions from experienced physicians.

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Rick Nauert PhD

Dr. Rick Nauert has over 25 years experience in clinical, administrative and academic healthcare. He is currently an associate professor for Rocky Mountain University of Health Professionals doctoral program in health promotion and wellness. Dr. Nauert began his career as a clinical physical therapist and served as a regional manager for a publicly traded multidisciplinary rehabilitation agency for 12 years. He has masters degrees in health-fitness management and healthcare administration and a doctoral degree from The University of Texas at Austin focused on health care informatics, health administration, health education and health policy. His research efforts included the area of telehealth with a specialty in disease management.

APA Reference Nauert PhD, R. (2018). AI Made for Military May Aid Bipolar Treatment. Psych Central.
Retrieved on January 21, 2019, from https://psychcentral.com/news/2017/06/13/ai-made-for-military-may-aid-bipolar-treatment/121880.html

Last updated: 8 Aug 2018Last reviewed: By a member of our scientific advisory board on 8 Aug 2018Published on Psych Central.com. All rights reserved.